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  1. Erweiterte Patentsuche
VeröffentlichungsnummerUS20060287989 A1
PublikationstypAnmeldung
AnmeldenummerUS 11/454,301
Veröffentlichungsdatum21. Dez. 2006
Eingetragen16. Juni 2006
Prioritätsdatum16. Juni 2005
Auch veröffentlicht unterUS9158855, US20160117390
Veröffentlichungsnummer11454301, 454301, US 2006/0287989 A1, US 2006/287989 A1, US 20060287989 A1, US 20060287989A1, US 2006287989 A1, US 2006287989A1, US-A1-20060287989, US-A1-2006287989, US2006/0287989A1, US2006/287989A1, US20060287989 A1, US20060287989A1, US2006287989 A1, US2006287989A1
ErfinderNatalie Glance
Ursprünglich BevollmächtigterNatalie Glance
Zitat exportierenBiBTeX, EndNote, RefMan
Externe Links: USPTO, USPTO-Zuordnung, Espacenet
Extracting structured data from weblogs
US 20060287989 A1
Zusammenfassung
A method of extracting individual posts from a weblog comprises the steps of: (a) providing a feed associated with the weblog; and (b) screen scraping the weblog into a representation for weblog posts using the feed data containing partial content of the weblog.
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Ansprüche(34)
1. A method of extracting individual posts from a weblog, comprising the steps of:
(a) accessing the home page of the weblog;
(b) identifying at least one feed associated with the weblog;
(c) determining whether the feed contains sufficient content for performing feed-guided segmentation;
(d) if the feed contains sufficient content for feed-guided segmentation, determining whether the feed contains full content or partial content of the weblog;
(e) if the feed contains full content of the weblog, mapping the data found in the feed into a representation for weblog posts; and
(f) if the feed contains partial content of the weblog, screen scraping the weblog into a representation for weblog posts using the feed data.
2. The method of claim 1, wherein
the identifying step (b) comprises receiving a feed URL for the weblog from a ping related by a ping server from which the weblog update is collected.
3. The method of claim 1, wherein
the identifying step (b) comprises mapping the weblog's URL to a feed URL, if the weblog is hosted by a weblog host that publishes a full content feed for the weblog.
4. The method of claim 1, wherein
the identifying step (b) comprises using RSS auto-discovery for identifying the the webog feed's URL from metadata in the HTML for the weblog's home page.
5. The method of claim 1, wherein
the identifying step (b) further comprises:
(b1) extracting hyperlinks from the body of the weblog; and
(b2) filtering the set of extracted hyperlinks using a classifier to identify hyperlinks that belong to feeds for the weblog.
6. The method of claim 5, wherein
a set of heuristics is used in the filtering step (b2) to identify a feed for the weblog from the extracted hyperlinks.
7. The method of claim 5, wherein
the filtering step (b2) uses at least one of the following criteria to identify a feed for the weblog:
(i) URLs that allow readers to subscribe to the feed in their RSS reader;
(ii) URLs matching the host name of the blog and having a common feed suffix taken from the set including {“atom.xml”, “.xml”, “.rss”, and “rdf”}; and
(iii) URLs matching the domain name of the blog and having a common feed prefixes taken from the set including {“xml” and “rss”}.
8. The method of claim 1, wherein
in the determining step (c), an item in the feed is deemed to contain sufficient content if it contains a date-posted field and either a content field or a description field.
9. The method of claim 1, wherein
the determining step (d) uses heuristics to determine whether the feed contains full content based on features of content and description text in the feed.
10. The method of claim 9, wherein
the features of content and description text include presence or absence of HTML tags, percent of posts ending in ellipses, and type of feed.
11. The method of claim 1, wherein
the screen scraping step (f) further comprises:
(f1) creating skeletal representations of the weblog's posts using data available from the feed.
12. The method of claim 11, wherein
for each post in the weblog, the skeletal representation includes at least one of the following data: the weblog's URL, the date on which the post was posted, partial content of the post, a title of the post, the author of the post, and a permalink.
13. The method of claim 12, wherein
the screen scraping step (f) further comprises:
(f2) removing summarization artifacts from the skeletal representations.
14. The method of claim 12, wherein
the screen scraping step (f) further comprises:
(f3) removing ads from the skeletal representations.
15. The method of claim 12, wherein
the screen scraping step (f) further comprises:
(f4) searching the home page of the weblog for content matching the skeletal representations.
16. The method of claim 15, wherein
the screen scraping step (f) further comprises:
(f5) upon finding content on the home page of the weblog matching a skeletal representation, identifying an enclosing node associated with the matching text in the tidied XHTML for the weblog page;
(f6) copying text contained within the enclosing node to the skeletal representation, thereby using said text as the full content of the post; and
(f7) repeating steps (f5) and (f4) for the skeletal representation of each post in the weblog.
17. The method of claim 1, further comprising the step of:
(g) if the feed contains insufficient partial content to perform screen scraping in step (f), repeating steps (c) through (f) using another feed associated with the weblog.
18. The method of claim 17, further comprising the step of:
(h) if the weblog contains no feeds with sufficient full content or sufficient partial content, performing screen scraping of the weblog into a representation for weblog posts.
19. The method of claim 18, wherein
the screen scraping step (h) further comprises:
(h1) extracting dates from the tidied XHTML for the weblog page;
(h2) sorting the extracted dates into ordered lists, each ordered list corresponding to a unique relative XPath;
(h3) filtering the ordered lists according to a set of heuristics to determine which list corresponds to the actual entry dates of the weblog posts;
(h4) segmenting the weblog into entries, using dates from the list obtained in step (h3) as markers for the entries;
(h5) segmenting each weblog entry into posts using post titles markers; and
(h6) identifying a permalink and author for each post.
20. The method of claim 19, wherein
the set of heuristics in the filtering step (h3) includes at least one of:
(i) keeping only lists whose dates all belong to the current year and/or the past year;
(ii) keeping only non-singleton date lists;
(iii) keeping only lists whose dates conform to a similar format (e.g. MM/dd/YYYY;
(iv) keeping only lists whose dates decrease monotonically;
(v) keeping only lists with most recent dates (but not in the future);
(vi) keeping only lists with longest date string representation;
(vii) keeping only lists with the greatest number of dates; and
(viii) keeping only first list.
21. The method of claim 19, wherein
the segmenting step (h4) uses a set of heuristics to identify the end of each entry.
22. The method of claim 21, wherein
the set of heuristics includes at least one of: a start of a sidebar on the weblog page, a copyright notice on the weblog page, a form, and a comment.
23. The method of claim 21, wherein
the set of heuristics includes a step of looking for a node in the document object model whose XPath is analogous in structure to the XPath of the last node in a previous weblog entry.
24. The method of claim 19, wherein
the segmenting step (h5) further comprises:
(i) searching the entry's nodes to identify a title node;
(ii) assuming that the titles of all subsequent posts have the same relative XPath as the title node identified in step (i); and
(iii) if no title node is identified, assuming that the weblog entry is a single post.
25. The method of claim 24, wherein
a node is identified as a title node if the node's class attribute is one of: title, subtitle, or blogpost.
26. The method of claim 19, wherein
the identifying step (h6) further comprises identifying patterns indicative of an author byline.
27. The method of claim 19, wherein
the identifying step (h6) further comprises identifying hrefs in the post content that match, thereby indicating a permalink.
28. A method of extracting individual posts from a weblog, comprising the steps of:
(a) providing a feed associated with the weblog; and
(b) screen scraping the weblog into a representation for weblog posts using the feed data containing partial content of the weblog.
29. The method of claim 28, wherein
the screen scraping step (b) further comprises:
(b1) creating skeletal representations of the weblog's posts using data available from the feed.
30. The method of claim 29, wherein
for each post in the weblog, the skeletal representation includes at least one of the following data: the weblog's URL, the date on which the post was posted, partial content of the post, a title of the post, the author of the post, and a permalink.
31. The method of claim 30, wherein
the screen scraping step (b) further comprises:
(b2) removing summarization artifacts from the skeletal representations.
32. The method of claim 30, wherein
the screen scraping step (b) further comprises:
(b3) removing ads from the skeletal representations.
33. The method of claim 30, wherein
the screen scraping step (b) further comprises:
(b4) searching the home page of the weblog for content matching the skeletal representations.
34. The method of claim 33, wherein
the screen scraping step (b) further comprises:
(b5) upon finding content on the home page of the weblog matching a skeletal representation, identifying an enclosing node associated with the matching text in the tidied XHTML for the weblog page;
(b6) copying text contained within the enclosing node to the skeletal representation, thereby using said text as the full content of the post; and
(b7) repeating steps (b5) and (b4) for the skeletal representation of each post in the weblog.
Beschreibung
    CROSS-REFERENCE TO RELATED APPLICATIONS
  • [0001]
    This application claims the priority of U.S. Provisional Patent Application Ser. No. 60/691,200, filed Jun. 16, 2005, the disclosure of which is incorporated here in by reference.
  • BACKGROUND OF THE INVENTION
  • [0002]
    Weblogging or “blogging” has emerged in the past few years as a new grassroots publishing medium. Like electronic mail and the web itself, weblogging has taken off and by some estimates the number of weblogs is doubling every 6 months. As of Jun. 2006, BlogPulse estimates place the number of active weblogs at nearly 10 million blogs, of which about 36% have had at least one post in the past 3 months. BlogPulse finds approximately 60,000 new weblogs each day. Statistics published by other blog search engines such as Technorati and PubSub are similar. However, these estimates may well be excluding large numbers of non-English language weblogs.
  • [0003]
    A weblog is commonly defined as a web page with a set of dated entries, in reverse chronological order, maintained by its writer via a weblog publishing software tool. We can define each entry as a set of one or more time-stamped posts; an author may typically post several times a day. This is a matter a style, as some authors post at most once a day in an all-inclusive entry. Others prefer to micro-post, making each published item a separate post in the day's entry.
  • [0004]
    Due to the popularity of weblogs, there is a need for a method of searching individual posts within weblogs. The present invention addresses this need.
  • SUMMARY
  • [0005]
    The invention described herein provides a process for segmenting weblogs into posts. The segmentation process described works with a large majority of blogs without requiring specialized handling for individual weblogs.
  • [0006]
    Accordingly, it is a first aspect of the present invention to provide a method of extracting individual posts from a weblog, including the steps of: (a)accessing the home page of the weblog; (b) identifying at least one feed associated with the weblog; (c) determining whether the feed contains sufficient content for performing feed-guided segmentation; (d) if the feed contains sufficient content for feed-guided segmentation, determining whether the feed contains full content or partial content of the weblog; (e) if the feed contains full content of the weblog, mapping the data found in the feed into a representation for weblog posts; and (f) if the feed contains partial content of the weblog, screen scraping the weblog into a representation for weblog posts using the feed data.
  • [0007]
    In detailed embodiments of the first aspect of the present invention, the identifying step (b) includes one of the following: receiving a feed URL for the weblog from a ping related by a ping server from which the weblog update is collected; mapping the weblog's URL to a feed URL, if the weblog is hosted by a weblog host that publishes a full content feed for the weblog; using RSS auto-discovery for identifying the the webog feed's URL from metadata in the HTML for the weblog's home page; and the combination of (b1) extracting hyperlinks from the body of the weblog and (b2) filtering the set of extracted hyperlinks using a classifier to identify hyperlinks that belong to feeds for the weblog.
  • [0008]
    In a detailed embodiment of the first aspect of the present invention, in the determining step (c), an item in the feed is deemed to contain sufficient content if it contains a date-posted field and either a content field or a description field.
  • [0009]
    In a detailed embodiment of the first aspect of the present invention, the determining step (d) uses heuristics to determine whether the feed contains full content based on features of content and description text in the feed. The features of content and description text can include presence or absence of HTML tags, percent of posts ending in ellipses, and type of feed.
  • [0010]
    In a detailed embodiment of the first aspect of the present invention, the screen scraping step (f) further includes (f1) creating skeletal representations of the weblog's posts using data available from the feed. For each post in the weblog, the skeletal representation can include at least one of the following data: the weblog's URL, the date on which the post was posted, partial content of the post, a title of the post, the author of the post, and a permalink. In further detailed embodiments, the screen scraping step (f) further includes one of: (f2) removing summarization artifacts from the skeletal representations; (f3) removing ads from the skeletal representations; and (f4) searching the home page of the weblog for content matching the skeletal representations. In further detailed embodiments, the screen scraping step (f) further includes: (f5) upon finding content on the home page of the weblog matching a skeletal representation, identifying an enclosing node associated with the matching text in the tidied XHTML for the weblog page; (f6) copying text contained within the enclosing node to the skeletal representation, thereby using said text as the full content of the post; and (f7) repeating steps (f5) and (f4) for the skeletal representation of each post in the weblog.
  • [0011]
    In a detailed embodiment of the first aspect of the present invention, the method can include one or both of the following steps: (g) if the feed contains insufficient partial content to perform screen scraping in step (f), repeating steps (c) through (f) using another feed associated with the weblog; and (h) if the weblog contains no feeds with sufficient full content or sufficient partial content, performing screen scraping of the weblog into a representation for weblog posts. The screen scraping step (h) can further include: (h1) extracting dates from the tidied XHTML for the weblog page; (h2) sorting the extracted dates into ordered lists, each ordered list corresponding to a unique relative XPath; (h3) filtering the ordered lists according to a set of heuristics to determine which list corresponds to the actual entry dates of the weblog posts; (h4) segmenting the weblog into entries, using dates from the list obtained in step (h3) as markers for the entries; (h5) segmenting each weblog entry into posts using post titles markers; and (h6) identifying a permalink and author for each post. The set of heuristics in the filtering step (h3) can include at least one of: (i) keeping only lists whose dates all belong to the current year and/or the past year; (ii) keeping only non-singleton date lists; (iii) keeping only lists whose dates conform to a similar format (e.g. MM/dd/YYYY); (iv) keeping only lists whose dates decrease monotonically; (v) keeping only lists with most recent dates (but not in the future); (vi) keeping only lists with longest date string representation; (vii) keeping only lists with the greatest number of dates; and (viii) keeping only first list. In a more detailed embodiment, the segmenting step (h4) uses a set of heuristics to identify the end of each entry. In a further more detailed embodiment, the set of heuristics includes at least one of: a start of a sidebar on the weblog page, a copyright notice on the weblog page, a form, and a comment. In another further more detailed embodiment, the set of heuristics includes a step of looking for a node in the document object model whose XPath is analogous in structure to the XPath of the last node in a previous weblog entry. In another more detailed embodiment, the segmenting step (h5) further includes: (i) searching the entry's nodes to identify a title node; (ii) assuming that the titles of all subsequent posts have the same relative XPath as the title node identified in step (i); and (iii) if no title node is identified, assuming that the weblog entry is a single post. In another more detailed embodiment, the identifying step (h6) further includes one of: identifying patterns indicative of an author byline; and identifying hrefs in the post content that match, thereby indicating a permalink.
  • [0012]
    It is a second aspect of the present invention to provide a method of extracting individual posts from a weblog, including the steps of: (a) providing a feed associated with the weblog; and (b) screen scraping the weblog into a representation for weblog posts using the feed data containing partial content of the weblog.
  • [0013]
    In adetailed embodiment of the second aspect of the present invention, the screen scraping step (b) further includes (b1) creating skeletal representations of the weblog's posts using data available from the feed. For each post in the weblog, the skeletal representation can include at least one of the following data: the weblog's URL, the date on which the post was posted, partial content of the post, a title of the post, the author of the post, and a permalink. In further detailed embodiments, the screen scraping step (b) further includes one of: (b2) removing summarization artifacts from the skeletal representations; (b3) removing ads from the skeletal representations; and (b4) searching the home page of the weblog for content matching the skeletal representations. In further detailed embodiments, the screen scraping step (b) further includes: (b5) upon finding content on the home page of the weblog matching a skeletal representation, identifying an enclosing node associated with the matching text in the tidied XHTML for the weblog page; (b6) copying text contained within the enclosing node to the skeletal representation, thereby using said text as the full content of the post; and (b7) repeating steps (b5) and (b4) for the skeletal representation of each post in the weblog.
  • [0014]
    These and other aspects and embodiments will be apparent from the following description, the accompanying drawings, and the appended claims.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • [0015]
    FIG. 1 shows a sample page from a weblog.
  • DETAILED DESCRIPTION
  • [0016]
    1. Overview
  • [0017]
    The present invention provides a process for segmenting weblogs into posts. Weblogs can facilitate communication and dissemination of content in any environment having two or more workstations in mutual communication. While weblogs are typically hosted by a server connected to the Internet, the concept can include other types of networks, such as local area networks (LANs), wide area networks (WANs), and public data networks, by which client workstations obtain data from a server workstation.
  • [0018]
    Each workstation may comprise a microcomputer such as a personal computer, for example, including a system bus that is connected to a central processing unit (CPU) and to memory, including read only memory (ROM) and random access memory (RAM). The system bus can be connected, via appropriate interface known to persons skilled in the art, to various input/output devices, including additional nonvolatile data storage devices, video and audio adapters, keyboard, mouse, and other devices that provide input to the workstation or receive output from the workstation. The Workstation can also include a data port for communicating with other constituents of collaborative data processing environment. The data port may be a serial port for linking workstation to a modem or a communications adapter for connecting workstation to a LAN.
  • [0019]
    Each workstation also typically includes software programs that are stored on the data storage devices or retrieved from other parts of collaborative data processing system and loaded into RAM and then into CPU for execution. Among those programs is a client program that receives messages from, and transmits messages to, other workstations connected to the network.
  • [0020]
    Web search engines such as Google, Yahoo, and MSN Search index the entire content of a web page typically every few days. However, for weblogs, users want to be able to search over individual posts, and in near real-time. Weblog search portals such as Technorati, Feedster, PubSub and BlogPulse have gained in popularity over the past year and a half, as people begin to turn to weblogs to get up-to-the-minute breaking news and to get fresh angles on news stories.
  • [0021]
    In addition, marketers have awakened to the possibility of mining consumer sentiment from weblogs. In order to produce accurate analytics, it is first necessary to be to identify individual weblog posts. Examples of consumer sentiment analytics are the buzz surrounding a product (number of mentions), number of links to a company website, trends in number of mentions and number of links, and ratio of positive vs. negative mentions. Glance, M. Hurst, K. Nigam, M. Siegler, R. Stockton, and T. Tomokiyo. Analyzing online discussion for marketing intelligence. In Proceedings WWW-2005, Chiba, Japan, 2005 (incorporated herein by reference).
  • [0022]
    Researchers as well are turning to blogs to gauge opinion and community structure. For example, Adamic and Glance recently analyzed the linking behavior of political bloggers during the 2004 U.S. Presidential Election and found that conservative bloggers link to each other more frequently and in a denser pattern than liberal bloggers. Adamic and N. Glance, The political blogosphere and the 2004 u.s. election: Divided they blog, In Proceedings WWW-2005 2nd Annual Workshop on the Weblogging Ecosystem, Chiba, Japan, 2005 (incorporated herein by reference). Marlow has studied the structure and authority in weblogs using inter-post citation counts. Marlow. Audience, structure and authority in the weblog community, In International Communication Association Conference, New Orleans, La., 2004 (incorporated herein by reference). Adar et. al. have explored how memes thread through the blogsphere from post to post. Adar, L. Zhang, L. A. Adamic, and R. M. Lukose, Implicit structure and the dynamics of blogspace, In Proceedings WWW-2004 Workshop on the Weblogging Ecosystem, New York City, N.Y., 2004 (incorporated herein by reference). The Global Attention Profiles project tracks the attention that bloggers pay to different nations of the world, in comparison with selected mainstream media outlets.
  • [0023]
    To enable sophisticated analytics over weblogs, a blog search engine typically uses an indexing mechanism that indexes a weblog one post at time, as opposed to one HTML page at a time. In order to index blogs one post at a time, the indexing system should be able to segment the weblog HTML into individual posts and extract meta-data associated with the posts, such as the posting date, title, permalink, and author.
  • [0024]
    The present invention provides a method for segmenting weblogs into individual posts using a combination of weblog feeds (such as RSS and Atom) and model-based wrapper segmentation. RSS is a family of web feed formats, specified in XML and used for Web syndication. Web feeds provide web content or summaries of web content together with links to the full versions of the content, and other metadata. RSS, in particular, delivers this information as an XML file called an RSS feed, webfeed, RSS stream, or RSS channel. In addition to facilitating syndication, web feeds allow a website's frequent readers to track updates on the site using an aggregator. Atom is the name of a specific web feed format. Web feeds, from a user's perspective, allow Internet users to subscribe to websites that change or add content regularly. Web feeds in general provide web content or summaries of web content together with links to the full versions of the content, and other meta-data in a developer-friendly standardized format Atom, from a technical perspective, is an open standard that includes an XML-based web syndication format used by weblogs, news websites and web mail.
  • [0025]
    2. Definitions
  • [0026]
    The following definitions are used throughout this description:
  • [0027]
    Weblog or blog: a weblog is a website where an individual or group of individuals publishes posts periodically. The posts are usually displayed in reverse chronological order. Each post generally consists of: a date, a title, the body of the post, a permalink to the post, an author, and one or more categorizations.
  • [0028]
    Weblog entry: a post or a set of posts published on a specific day.
  • [0029]
    Post: item published to weblog at a specific time of day.
  • [0030]
    Weblog feed/syndication: weblogs may or may not make posts available via syndication using RSS or Atom feeds. Web feeds provide web content or summaries of web content together with links to the full versions of the content, and other metadata. Atom feeds are XML documents. In addition, there are several versions of the RSS standard in use.
  • [0031]
    Weblog host: a company or website that hosts weblogs for individuals. Examples of popular weblog hosts are: livejournal.com, xanga.com, spaces.msn.com, blogspot.com, and the family of per-country domain typepad hosts.
  • [0032]
    Weblog software: software that enables creation and publishing of weblog posts to a weblog host, or to a self-hosted weblog. Each weblog host has its own weblog software tool for publishing posts. In addition, there are a number of weblog software tools for publishing a self-hosted weblog, such as Typepad, Moveable Type, and Wordpress.
  • [0033]
    Weblog ping: A weblog ping is an XML-RPC mechanism that notifies a ping server, such as weblogs.com or blo.gs, that the weblog has changed (e.g., the author has written a new post). Many weblog software tools can be set (or are automatically pre-set) to ping centralized servers whenever the weblog is updated. Example ping servers are http://blogs/ping.php and http://rpc.technorati.com/rpc/pingl. Some ping servers accept “extended pings” that include both the URL and feed URL of the updated weblog.
  • [0034]
    Crawl: A web crawler (also known as a web spider or web robot) is a program which browses the World Wide Web in a methodical, automated manner. A web crawler is one type of bot, or software agent. In general, it starts with a list of URLs to visit, called the seeds. As the crawler visits these URLs, it identifies all the hyperlinks in the page and adds them to the list of URLs to visit, called the crawl frontier. URLs from the frontier are recursively visited according to a set of policies.
  • [0035]
    Screen scraping: a technique in which a computer program extracts data from the display output of another program. The program doing the scraping is called a screen scraper. The key element that distinguishes screen scraping from regular parsing is that the output being scraped was nominally intended for human consumption, not machine interpretation. There are a number of synonyms for screen scraping, including: Data scraping, data extraction, web scraping, page scraping, and HTML scraping (the last three being specific to scraping web pages).
  • [0036]
    Wrapper: a program that performs screen scraping.
  • [0037]
    “Document Object Model” (DOM): a description of how an HTML or XML document is represented in an object-oriented fashion. DOM provides an application programming interface to access and modify the content, structure and style of the document.
  • [0038]
    Permalink: a term used in the world of blogging to indicate a URL which points to a specific blog entry.
  • [0039]
    XPath (XML Path Language): a terse (non-XML) syntax for addressing portions of an XML document.
  • [0040]
    3. Process for Extracting Posts from a Weblog
  • [0041]
    Here we describe a process for extracting individual posts from a weblog, according to an exemplary embodiment of the present invention. First we describe the typical layout of a weblog.
  • [0042]
    3.1. Modelling Weblogs
  • [0043]
    FIG. 1 shows the home page of a well-known weblog. Notice the extraneous content on the page: header, footer (not displayed) and sidebars (in this example, ads). However, the main content is a sequence of entries ordered in reverse chronological order, with each entry consisting of sequence of posts, also in reverse chronological order.
  • [0044]
    A weblog can be described formally as follows:
      • Weblog: Entry+
      • Entry: Date Post+
      • Post: Title? Content Permalink? Author? Timestamp? Link to comments?
        Categories*
  • [0048]
    The ordering of the sub-elements for the Entry elements and the Post elements is typically not standardized across weblogs, although it is assumed to be fixed within a weblog.
  • [0049]
    Also, the model assumes that the entry dates are monotonically decreasing.
  • [0050]
    3.2. Weblog Syndication
  • [0051]
    Many weblog publishing software tools also publish a feed in association with the weblog. The feed is updated whenever a new item is posted to the weblog. The feed is a “pull” mechanism, as is the weblog page. As a “pull” mechanism, the feed is accessed in order to find out if the weblog has been updated. However, feeds are designed to be read via a feed reader/aggregator (such as Bloglines, NewsGator, etc. or via an extension to a mail reader), which polls the feed on the behalf of the user(s). Thus, the end user who reads feeds via a feed reader experiences weblogs as a “push” phenomena: the newly published weblog posts are pushed to the user's screen.
  • [0052]
    Some weblog software tools have provided customization of the weblog's feed: the publication of the feed can be turned on or off, the feed can be updated whenever a new item is posted or modified, and the feed can be full content or partial content. Full vs. partial content is an important distinction. We define a full content feed as a feed that publishes the entire content of the post as viewable on the front page of the weblog. We define a partial content feed as a feed that publishes a summary of the post content available via the weblog.
  • [0053]
    With respect to feed publication, weblog software tools fall into three categories: (1) automatic generation of feeds (partial or full); (2) customized generation of feeds; or (3) no feed generation capability. In the last case, some tech-savvy bloggers will use custom software to create a feed and associate it with their weblog, or turn to a third-party feed generator to host a feed for the weblog (e.g., FeedBurner: http://www.feedburner.com/).
  • [0054]
    3.3. Segmenting Weblogs into Posts
  • [0055]
    This section describes our approach for segmenting weblogs into posts, according to an exemplary embodiment of the present invention. It would be costly to manually create individual wrappers for each weblog. However, weblogs tend to conform to a common model, as described in Section 3.1 above. Thus, we have focused on developing an approach that generalizes well over the majority of weblogs.
  • [0056]
    If a full content feed is available for a weblog, then the task of extracting posts from the weblog is the straightforward mapping of the XML format to an internal format. If a partial content feed exists for a weblog, then we use the partial content to guide the extraction process. If no partial content feed exists for a weblog, then we apply a model-based approach to extracting posts from the weblog page, taking advantage of regularities more or less common to most weblogs. Our work on model-based segmentation is similar to that of Nanno et al. Nanno, Automatic collection and monitoring of japanese weblogs, In Proceedings WWW-2004 Workshop on the Weblogging Ecosystem, New York City, N.Y., 2004 (incorporated herein by reference).
  • [0057]
    Accordingly, here is an outline of the algorithm used for extracting posts from a weblog, according to an exemplary embodiment of the present invention:
  • [0058]
    1. Crawl home page of weblog.
  • [0059]
    2. Discover feed(s) associated with weblog
  • [0060]
    3. For each feed:
        • (a) Determine if feed satisfies minimal requirements for proceeding. Our feed finder considers an item in the feed sufficient if it contains, at minimum, the following fields: date-posted AND (content OR description).
        • (b) If the feed is sufficient, classify the feed as full content or partial content.
        • (c) If feed is full content, then we map the data found in the feed into a representation for weblog posts.
        • (d) If feed is partial content, then use feed data to guide screen scraping of the weblog to construct a representation for weblog posts.
        • (e) If the feed has insufficient content, then try next feed associated with weblog.
      • 4. If there are no feeds with sufficient full or partial content, then fall back on screen scraping of weblog. Screen scraping uses a model-based approach to segment the weblog page into posts using textual and HTML elements of the page as markers.
  • [0067]
    3.4. Feed Discovery
  • [0068]
    After reaching the home page of the weblog, the first step consists of discovering the feed(s) for the weblog. If the weblog update was collected from a ping server relaying extended pings, and if the accepted ping includes the feed URL for the weblog, then we have located the feed. Alternatively, if the weblog is hosted by a weblog host which publishes full content feeds for its weblogs, then we need only map the weblog URL to the feed URL.
  • [0069]
    Otherwise, the next step in discovering the feed(s) for a weblog is to use “RSS auto-discovery.” RSS auto-discovery is an agreed-upon standard for specifying the location(s) of a webogs feed(s) as metadata in the HTML for the weblog home page.
  • [0070]
    If RSS auto-discovery fails to find a set of feeds for the weblog, the next step is to search for links to feeds from body of the weblog. First, all hyperlinks are extracted from the weblog. Next, the set of extracted hyperlinks are filtered using a classifier to identify which one(s) belong to the set of feeds for the weblogs. Currently, we use a set of heuristics to identify the feed(s) for a weblog from the extracted hyperlinks. The following is a non-exclusive list of criteria that can be used to identify the feed:
      • URLs that allow readers to subscribe to the feed in their RSS reader; these urls match “?url=?” or “bloglines.com/sub/”
      • URLs with one of a set of common feed suffixes, including {“atom.xml”, “.xml”, “.rss”, “rdf”, . . . } AND matching the host name of the blog
      • URLs with a host with one of a set of common feed prefixes, including {“xml”, “rss”, . . . } AND matching the domain name of the blog.
  • [0074]
    3.5. Full Content vs. Partial Content Feeds
  • [0075]
    The multiple XML standards for weblog feeds (several versions of RSS and Atom) all satisfy the following minimal conditions:
      • The feed has the following top-level fields: weblog url, weblog title
      • The feed consists of a set of items (which for weblogs, correspond to posts). Each item may have the following fields: date-posted, permalink, post title, author, content, description
  • [0078]
    Our feed finder considers an item in the feed content to be sufficient if it contains, at minimum, the following fields: date-posted AND (content OR description). If no items in the feed contain sufficient content, the feed is rejected and weblog segmentation falls back upon model-based weblog segmentation (aka screen scraping).
  • [0079]
    The actual names of the fields depend on the feed standard being used. For example, for RSS v0.91, date-posted maps onto the XPath/item/title; content maps onto the XPath/item/description; and description maps onto the XPath /item/description. (There is no separate content field in the RSS v0.91 specification.)
  • [0080]
    Typically, the description field is used to provide a summary of the post (usually the first few lines) while the content field is used to provide either the full content of the post or a summary. Some feeds contain both, in which case, typically, the description contains the summary and the content contains the full post.
  • [0081]
    The feed classifier, which classifies the feed as full content or partial content, takes as input features of the content and description text, such as: presence/absence of HTML tags, % posts ending in ellipses, and type of feed. Based on these features, it uses heuristics decides whether or not the items in the feed are full content. Other features could be added, such as the variance in the length of text, etc.
  • [0082]
    If the feed is classified as full content, then we map the data found in the feed into our own internal representation for weblog posts, using XML representation of the content of the post +meta-data. Elements in the XML representation include: weblog url, permalink, weblog title, post title, date posted, time posted, and content.
  • [0083]
    If the feed is not full content, then we create skeletal posts from the data in the feed. For each post, we fill in the following data: weblog url; date-posted; partial content; post title (if found); post author (if found); and permalink (if found).
  • [0084]
    3.6. Feed-guided Weblog Segmentation
  • [0085]
    The next step is to fill in the skeletal posts constructed from the feed by using the content of the weblog page itself. Missing from the skeletal posts is the full content of the post. To find the full content, the partial content is first processed to remove summarization artifacts (e.g., ending ellipsis). Then, we search for the partial content in the weblog. If the partial content is not found, then we will omit that particular post from our segmentation because not enough information can be located to construct the post. If we end up finding insufficient information on all posts, then we will fall back on model-based segmentation.
  • [0086]
    If the partial content matches text on the weblog home page, then we find the enclosing node for the matching text in the tidied XHTML for the weblog page. The Extensible HyperText Markup Language, or XHTML, is a markup language that has the same expressive possibilities as HTML, but a stricter syntax. The text inside the enclosing node is then used as the content for the post. If enclosing nodes for successive posts overlap, then we throw an error indicating that feed-guided segmentation has failed for the weblog, and, again, fall back on model-based segmentation.
  • [0087]
    3.7. Model-based Weblog Segmentation
  • [0088]
    If there are no feeds with sufficient full or partial content, then we attempt to segment the weblog into posts using screen scraping of the weblog. Screen scraping uses a model-based approach to segment the weblog page into posts using textual and HTML elements of the page as markers.
  • [0089]
    Model-based weblog segmentation assumes that weblogs can be modeled as described in Section 3. 1. Our approach then starts from a simplification of that model: (date ([title] content)+)+. This model assumes that dates appear first. This means that if we are able to extract the weblog entry dates, then we can use the dates as markers for the entries. Of course, a weblog page may have many other dates apart from the dates marking the entries: dates in the content of the posts; dates in the sidebars or in other non-weblog content included in the HTML page. However, as weblogs are produced by weblog software, we can expect certain regularities in the underlying DOM of the generated HTML. In particular, we expect that the relative XPaths of the weblog entry dates to be identical. A relative XPath is an Xpath that is defined relative to a location (XML node) in an XML document. In practice we've found that the relative XPaths of the entry dates are identical if we ignore certain elements in the XPath:/align/and repeating/font/s.
  • [0090]
    The first step in our model-based segmentation algorithm consists of extracting all the dates from the tidied XHTML for the weblog page using a date extractor. The dates are sorted into ordered lists, one list for each unique relative XPath. The order within the list corresponds to the ordering of the dates with the DOM for the weblog page.
  • [0091]
    We then filter the lists according to a set of heuristics in order to identify which list corresponds to the actual weblog entry dates. The filtering process for the date lists can be performed using the following sequence of steps:
  • [0092]
    11. Keep only lists whose dates all belong to the current year and/or the past year.
      • 2. Keep only non-singleton date lists.
      • 3. Keep only lists whose dates conform to a similar format (e.g. MM/dd/YYYY).
      • 4. Keep only lists whose dates decrease monotonically.
      • 5. Keep only lists with most recent dates (but not in the future).
      • 6. Keep only lists with longest date string representation.
      • 7. Keep only lists with the greatest number of dates.
      • 2. Keep only first list.
  • [0100]
    One might think that after step 5 in the filtering process, we would be left with at most one list of dates. In practice, this is frequently not the case, because weblogs often have a sidebar with a dated list of recent posts which corresponds exactly the full set of posts in the main part of the weblog. The last few filtering steps help correctly identify the weblog entry dates as opposed to the dates in the sidebar.
  • [0101]
    If we fail to find a conforming list of dates, then model-based segmentation fails. There are some known cases where our approach fails: when only one entry appears on the home page of the weblog; or when weblog software for some reason generates irregular XPaths for the dates and/or content. But in many cases, segmentation fails when the HTML page in question is not actually a weblog. Thus, our model-based segmentation algorithm has the additional functionality of serving as a classifier that identifies whether or not an HTML page is indeed a weblog.
  • [0102]
    Once we have identified the entry dates for the weblog, model-based segmentation proceeds as follows:
      • 1. Segment weblog into entries, using dates as markers.
      • 2. Segment each weblog entry into posts using post titles markers.
      • 3. For each post, identify permalink and author.
  • [0106]
    In step 1, we assume that all DOM nodes between subsequent entry dates form the weblog entry associated with the earlier date. The main difficulty is identifying the end of the last post. For this we use a set of heuristics to identify the end of the blog entry by looking for the start of boilerplate weblog end template. Example end markers include: the start of a sidebar, a copyright notice, or a form, or a comment. Another heuristic for finding the end of the blog entry is to look for a node in the DOM whose XPath is analogous in structure to the XPath of the last node in the previous weblog entry.
  • [0107]
    In step 2, we attempt to use post titles to demarcate boundaries between posts for an entry. First, we iterate over the nodes of the entry searching for a node that matches one of our conditions for being a title node. These conditions include: class attribute of the node equals ‘title’ or ‘subtitle’ or ‘blogpost’, etc. Once we have found the first matching title, we then assume that all subsequent post titles will have the same relative XPath. Again, we assume that all DOM nodes between subsequent title nodes are associated with the earlier title.
  • [0108]
    If we are unable to find titles, then we treat the entire entry as a single post. In fact, we have found that the majority of bloggers do not post more than once per day.
  • [0109]
    The final post-processing step identifies the permalink and author from the content of each extracted post using common patterns for permalinks and author signatures. To find authors, we look for patterns like “posted by.” To find permalinks, we look for hrefs (hyperlinks) in the post content that match, for example, “comment” or “archive.” Some patterns are given higher priority than others for matching against permalinks.
  • [0110]
    A weakness of our current implementation of model-based wrapper segmentation is that it assumes that the date field comes first in a weblog entry. In fact, while most blogs exhibit the pattern date ([title] content)+, others use (title date content)+ or even ([title] content date)+. Our approach is still able to segment blogs exhibiting these less common patterns, although the segmentation associates the date with the incorrect content. That is, if we have a sequence of N posts (post 1 through post N), the date for post 1 will be associated with the content of post 2 and so on. In addition, we will fail to extract the content of post 1. We call this error a parity error.
  • [0111]
    4. Segmentation Statistics
  • [0112]
    We have implemented weblog segmentation as part of the BlogPulse weblog post collection, indexing and search system.
  • [0113]
    In tests of the model-based segmentation algorithm, we have found that the precision of this algorithm is about 90%—that is about 90% of extracted posts have date, title and content fields that correspond to those of actual posts on the weblogs. The recall is approximately 70%—that is, we are able to extract posts from about 70% of true weblogs.
    TABLE 1
    Segmentation statistics for Apr. 13, 2005
    Segmentation method % of weblogs
    Full content feed 78%
    Feed-guided segmentation 11%
    Model-based segmentation 11%
  • [0114]
    Table 1 shows the statistics for our segmentation process, the percentage of weblogs segmented using: (1) full content feeds (78%); (2) feed-guided segmentation (11%); or (3) model-based segmentation (11%).
  • [0115]
    We have implemented our segmentation algorithm as part of the weblog post collection subsytem of BlogPulse. This enables BlogPulse to provide search over individual blog posts. Furthermore, the corpus of dated weblog posts serves as a data set for tracking trends over time, and for analyzing how memes spread through the blogosphere.
  • [0116]
    Having described the invention with reference to embodiments, it is to be understood that the invention is defined by the claims, and it is not intended that any limitations or elements describing the embodiments set forth herein are to be incorporated into the meanings of the claims unless such limitations or elements are explicitly listed in the claims. Likewise, it is to be understood that it is not necessary to meet any or all of the identified advantages or objects of the invention disclosed herein in order to fall within the scope of any claims, since the invention is defined by the claims and since inherent and/or unforeseen advantages of the present invention may exist even though they may not have been explicitly discussed herein.
Patentzitate
Zitiertes PatentEingetragen Veröffentlichungsdatum Antragsteller Titel
US3950618 *27. Dez. 197213. Apr. 1976Bloisi Albertoni De LemosSystem for public opinion research
US5041972 *15. Apr. 198820. Aug. 1991Frost W AlanMethod of measuring and evaluating consumer response for the development of consumer products
US5077785 *13. Juli 199031. Dez. 1991Monson Gerald DSystem for recording comments by patrons of establishments
US5124911 *15. Apr. 198823. Juni 1992Image Engineering, Inc.Method of evaluating consumer choice through concept testing for the marketing and development of consumer products
US5371673 *7. Apr. 19936. Dez. 1994Fan; David P.Information processing analysis system for sorting and scoring text
US5495412 *15. Juli 199427. Febr. 1996Ican Systems, Inc.Computer-based method and apparatus for interactive computer-assisted negotiations
US5519608 *24. Juni 199321. Mai 1996Xerox CorporationMethod for extracting from a text corpus answers to questions stated in natural language by using linguistic analysis and hypothesis generation
US5537618 *22. Dez. 199416. Juli 1996Diacom Technologies, Inc.Method and apparatus for implementing user feedback
US5659732 *17. Mai 199519. Aug. 1997Infoseek CorporationDocument retrieval over networks wherein ranking and relevance scores are computed at the client for multiple database documents
US5659742 *15. Sept. 199519. Aug. 1997Infonautics CorporationMethod for storing multi-media information in an information retrieval system
US5668953 *22. Febr. 199516. Sept. 1997Sloo; Marshall AllanMethod and apparatus for handling a complaint
US5671333 *7. Apr. 199423. Sept. 1997Lucent Technologies Inc.Training apparatus and method
US5675710 *7. Juni 19957. Okt. 1997Lucent Technologies, Inc.Method and apparatus for training a text classifier
US5696962 *8. Mai 19969. Dez. 1997Xerox CorporationMethod for computerized information retrieval using shallow linguistic analysis
US5754939 *31. Okt. 199519. Mai 1998Herz; Frederick S. M.System for generation of user profiles for a system for customized electronic identification of desirable objects
US5761383 *3. Juni 19972. Juni 1998Northrop Grumman CorporationAdaptive filtering neural network classifier
US5794412 *13. März 199718. Aug. 1998Co.Me.Sca. Costruzioni Meccaniche Scarperia S.R.L.Method of folding a portion of dielectric sheet material
US5819285 *20. Sept. 19956. Okt. 1998Infonautics CorporationApparatus for capturing, storing and processing co-marketing information associated with a user of an on-line computer service using the world-wide-web.
US5822744 *15. Juli 199613. Okt. 1998Kesel; BradConsumer comment reporting apparatus and method
US5836771 *2. Dez. 199617. Nov. 1998Ho; Chi FaiLearning method and system based on questioning
US5884302 *17. Jan. 199716. März 1999Ho; Chi FaiSystem and method to answer a question
US5895450 *14. Juli 199720. Apr. 1999Sloo; Marshall A.Method and apparatus for handling complaints
US5911043 *1. Okt. 19968. Juni 1999Baker & Botts, L.L.P.System and method for computer-based rating of information retrieved from a computer network
US5924094 *1. Nov. 199613. Juli 1999Current Network Technologies CorporationIndependent distributed database system
US5950172 *19. Juli 19967. Sept. 1999Klingman; Edwin E.Secured electronic rating system
US5953718 *12. Nov. 199714. Sept. 1999Oracle CorporationResearch mode for a knowledge base search and retrieval system
US5974412 *24. Sept. 199726. Okt. 1999Sapient Health NetworkIntelligent query system for automatically indexing information in a database and automatically categorizing users
US6026387 *7. Okt. 199815. Febr. 2000Kesel; BradConsumer comment reporting apparatus and method
US6029195 *5. Dez. 199722. Febr. 2000Herz; Frederick S. M.System for customized electronic identification of desirable objects
US6035294 *3. Aug. 19987. März 2000Big Fat Fish, Inc.Wide access databases and database systems
US6061789 *12. Jan. 19969. Mai 2000International Business Machines CorporationSecure anonymous information exchange in a network
US6064980 *17. März 199816. Mai 2000Amazon.Com, Inc.System and methods for collaborative recommendations
US6067539 *2. März 199823. Mai 2000Vigil, Inc.Intelligent information retrieval system
US6078892 *9. Apr. 199820. Juni 2000International Business Machines CorporationMethod for customer lead selection and optimization
US6081793 *30. Dez. 199727. Juni 2000International Business Machines CorporationMethod and system for secure computer moderated voting
US6119933 *16. Juli 199819. Sept. 2000Wong; Earl ChangMethod and apparatus for customer loyalty and marketing analysis
US6233575 *23. Juni 199815. Mai 2001International Business Machines CorporationMultilevel taxonomy based on features derived from training documents classification using fisher values as discrimination values
US6236977 *4. Jan. 199922. Mai 2001Realty One, Inc.Computer implemented marketing system
US6236980 *9. Apr. 199822. Mai 2001John P ReeseMagazine, online, and broadcast summary recommendation reporting system to aid in decision making
US6260041 *30. Sept. 199910. Juli 2001Netcurrents, Inc.Apparatus and method of implementing fast internet real-time search technology (first)
US6266664 *1. Okt. 199824. Juli 2001Rulespace, Inc.Method for scanning, analyzing and rating digital information content
US6304864 *20. Apr. 199916. Okt. 2001Textwise LlcSystem for retrieving multimedia information from the internet using multiple evolving intelligent agents
US6314420 *3. Dez. 19986. Nov. 2001Lycos, Inc.Collaborative/adaptive search engine
US6324648 *23. Dez. 199927. Nov. 2001Gte Service CorporationSecure gateway having user identification and password authentication
US6362837 *5. Mai 199826. März 2002Michael GinnMethod and apparatus for simultaneously indicating rating value for the first document and display of second document in response to the selection
US6385586 *28. Jan. 19997. Mai 2002International Business Machines CorporationSpeech recognition text-based language conversion and text-to-speech in a client-server configuration to enable language translation devices
US6411936 *5. Febr. 199925. Juni 2002Nval Solutions, Inc.Enterprise value enhancement system and method
US6434549 *13. Dez. 199913. Aug. 2002Ultris, Inc.Network-based, human-mediated exchange of information
US6493703 *11. Mai 199910. Dez. 2002Prophet Financial SystemsSystem and method for implementing intelligent online community message board
US6507866 *19. Juli 199914. Jan. 2003At&T Wireless Services, Inc.E-mail usage pattern detection
US6510513 *13. Jan. 199921. Jan. 2003Microsoft CorporationSecurity services and policy enforcement for electronic data
US6519631 *23. Aug. 199911. Febr. 2003Atomica CorporationWeb-based information retrieval
US6539375 *4. Aug. 199925. März 2003Microsoft CorporationMethod and system for generating and using a computer user's personal interest profile
US6546390 *11. Juni 19998. Apr. 2003Abuzz Technologies, Inc.Method and apparatus for evaluating relevancy of messages to users
US6553358 *20. Apr. 199922. Apr. 2003Microsoft CorporationDecision-theoretic approach to harnessing text classification for guiding automated action
US6571234 *11. Mai 199927. Mai 2003Prophet Financial Systems, Inc.System and method for managing online message board
US6571238 *11. Juni 199927. Mai 2003Abuzz Technologies, Inc.System for regulating flow of information to user by using time dependent function to adjust relevancy threshold
US6576614 *20. Okt. 199910. Juni 2003Vertex Pharmaceuticals IncorporatedPeptide analogs as irreversible interleukin-1β protease inhibitors
US6622140 *15. Nov. 200016. Sept. 2003Justsystem CorporationMethod and apparatus for analyzing affect and emotion in text
US6658359 *15. Mai 20022. Dez. 2003Conti Temic Microelectronic GmbhMethod for determining the viscosity of an operating liquid of an internal combustion engine
US6662170 *22. Aug. 20009. Dez. 2003International Business Machines CorporationSystem and method for boosting support vector machines
US6678516 *21. Mai 200113. Jan. 2004Nokia CorporationMethod, system, and apparatus for providing services in a privacy enabled mobile and Ubicom environment
US6721734 *18. Apr. 200013. Apr. 2004Claritech CorporationMethod and apparatus for information management using fuzzy typing
US6751606 *23. Dez. 199815. Juni 2004Microsoft CorporationSystem for enhancing a query interface
US6751683 *29. Sept. 200015. Juni 2004International Business Machines CorporationMethod, system and program products for projecting the impact of configuration changes on controllers
US6757646 *25. Sept. 200129. Juni 2004Insightful CorporationExtended functionality for an inverse inference engine based web search
US6782393 *31. Mai 200024. Aug. 2004Ricoh Co., Ltd.Method and system for electronic message composition with relevant documents
US6807566 *16. Aug. 200019. Okt. 2004International Business Machines CorporationMethod, article of manufacture and apparatus for processing an electronic message on an electronic message board
US6889325 *28. Apr. 20003. Mai 2005Unicate BvTransaction method and system for data networks, like internet
US6892944 *30. Sept. 200217. Mai 2005Amerasia International Technology, Inc.Electronic voting apparatus and method for optically scanned ballot
US6928526 *20. Dez. 20029. Aug. 2005Datadomain, Inc.Efficient data storage system
US6934460 *4. Okt. 200423. Aug. 2005Digital Optics CorpCompensation and/or variation of wafer level produced lenses and resultant structures
US6978292 *21. Sept. 200020. Dez. 2005Fujitsu LimitedCommunication support method and system
US6983320 *23. Mai 20003. Jan. 2006Cyveillance, Inc.System, method and computer program product for analyzing e-commerce competition of an entity by utilizing predetermined entity-specific metrics and analyzed statistics from web pages
US6999914 *28. Sept. 200014. Febr. 2006Manning And Napier Information Services LlcDevice and method of determining emotive index corresponding to a message
US7043760 *3. Juli 20019. Mai 2006David H. HoltzmanSystem and method for establishing and managing relationships between pseudonymous identifications and memberships in organizations
US7117368 *22. Jan. 20013. Okt. 2006Nec CorporationAnonymous participation authority management system
US7146416 *1. Sept. 20005. Dez. 2006Yahoo! Inc.Web site activity monitoring system with tracking by categories and terms
US7185065 *13. Juni 200127. Febr. 2007Buzzmetrics LtdSystem and method for scoring electronic messages
US7188078 *28. Sept. 20056. März 2007Buzzmetrics, Ltd.System and method for collection and analysis of electronic discussion messages
US7188079 *28. Sept. 20056. März 2007Buzzmetrics, Ltd.System and method for collection and analysis of electronic discussion messages
US20010011351 *22. Jan. 20012. Aug. 2001Nec CorporationAnonymous participation authority management system
US20010018858 *9. Apr. 20016. Sept. 2001Dwek Norman ScottMultimedia content delivery system and method
US20010020228 *22. März 20016. Sept. 2001International Business Machines CorporationUmethod, system and program for managing relationships among entities to exchange encryption keys for use in providing access and authorization to resources
US20010034708 *28. Dez. 200025. Okt. 2001Walker Jay S.Method and system for establishing and maintaining user-controlled anonymous communications
US20020010691 *15. März 200124. Jan. 2002Chen Yuan YanApparatus and method for fuzzy analysis of statistical evidence
US20020019764 *5. Juli 200114. Febr. 2002Desmond MascarenhasSystem and method for anonymous transaction in a data network and classification of individuals without knowing their real identity
US20020103801 *31. Jan. 20011. Aug. 2002Lyons Martha L.Centralized clearinghouse for community identity information
US20030034393 *26. Sept. 200220. Febr. 2003Chung Kevin Kwong-TaiElectronic voting apparatus, system and method
US20030046144 *28. Aug. 20016. März 2003International Business Machines CorporationSystem and method for anonymous message forwarding and anonymous voting
US20030062411 *30. Sept. 20023. Apr. 2003Chung Kevin Kwong-TaiElectronic voting apparatus and method for optically scanned ballot
US20030088532 *20. Aug. 20028. Mai 2003Hampshire John B.Method and apparatus for learning to classify patterns and assess the value of decisions
US20030094489 *16. Apr. 200122. Mai 2003Stephanie WaldVoting system and method
US20030173404 *10. Apr. 200318. Sept. 2003Chung Kevin Kwong-TaiElectronic voting method for optically scanned ballot
US20040181675 *10. März 200416. Sept. 2004Hansen Marc WilliamProcess for verifying the identity of an individual over a computer network, which maintains the privacy and anonymity of the individual's identity characteristic
US20060004691 *20. Juni 20055. Jan. 2006Technorati Inc.Ecosystem method of aggregation and search and related techniques
US20060015737 *16. Juli 200319. Jan. 2006Sebastien CanardList signature method and application to electronic voting
US20060173985 *10. Sept. 20053. Aug. 2006Moore James FEnhanced syndication
US20060206505 *30. Juni 200514. Sept. 2006Adam HyderSystem and method for managing listings
Referenziert von
Zitiert von PatentEingetragen Veröffentlichungsdatum Antragsteller Titel
US7421429 *4. Aug. 20052. Sept. 2008Microsoft CorporationGenerate blog context ranking using track-back weight, context weight and, cumulative comment weight
US7565350 *19. Juni 200621. Juli 2009Microsoft CorporationIdentifying a web page as belonging to a blog
US764735114. Sept. 200612. Jan. 2010Stragent, LlcWeb scrape template generation
US7720835 *7. Mai 200718. Mai 2010Visible Technologies LlcSystems and methods for consumer-generated media reputation management
US7765209 *13. Sept. 200527. Juli 2010Google Inc.Indexing and retrieval of blogs
US784058227. März 200823. Nov. 2010International Business Machines CorporationSystem and method for retrieving information from the internet by means of an intelligent search agent
US811719522. März 200614. Febr. 2012Google Inc.Providing blog posts relevant to search results
US815087815. Nov. 20073. Apr. 2012Yahoo! Inc.Device method and computer program product for sharing web feeds
US816106623. Jan. 200917. Apr. 2012Evri, Inc.Methods and systems for creating a semantic object
US819068416. Apr. 200829. Mai 2012Evri Inc.Methods and systems for semantically managing offers and requests over a network
US819108223. Okt. 200729. Mai 2012International Business Machines CorporationSystem and method for accessing really simple syndication (RSS) enabled content using session initiation protocol (SIP) signaling
US820061722. Juni 200912. Juni 2012Evri, Inc.Automatic mapping of a location identifier pattern of an object to a semantic type using object metadata
US823006221. Juni 201024. Juli 2012Salesforce.Com, Inc.Referred internet traffic analysis system and method
US8244720 *13. Sept. 200514. Aug. 2012Google Inc.Ranking blog documents
US827579622. Aug. 200825. Sept. 2012Evri Inc.Semantic web portal and platform
US8280872 *23. Aug. 20072. Okt. 2012Adobe Systems IncorporatedAutomated routing of forum postings
US834732618. Dez. 20071. Jan. 2013The Nielsen Company (US)Identifying key media events and modeling causal relationships between key events and reported feelings
US8402021 *31. Juli 200919. März 2013Google Inc.Providing posts to discussion threads in response to a search query
US841270429. Dez. 20112. Apr. 2013Google Inc.Providing blog posts relevant to search results
US842901120. Jan. 200923. Apr. 2013Salesforce.Com, Inc.Method and system for targeted advertising based on topical memes
US843812418. Okt. 20077. Mai 2013Evri Inc.System and method of a knowledge management and networking environment
US845858418. Nov. 20104. Juni 2013Google Inc.Extraction and analysis of user-generated content
US847347316. März 201025. Juni 2013Microsoft CorporationObject oriented data and metadata based search
US8566418 *4. Mai 200622. Okt. 2013Samsung Electronics Co., LtdConfigurable system for using Internet services on CE devices
US863097221. Juni 200814. Jan. 2014Microsoft CorporationProviding context for web articles
US868331111. Dez. 200925. März 2014Microsoft CorporationGenerating structured data objects from unstructured web pages
US8706757 *9. Okt. 200722. Apr. 2014Yahoo! Inc.Device, method and computer program product for generating web feeds
US879371520. Nov. 201229. Juli 2014The Nielsen Company (Us), LlcIdentifying key media events and modeling causal relationships between key events and reported feelings
US8838625 *3. Apr. 200916. Sept. 2014Yahoo! Inc.Automated screen scraping via grammar induction
US886257914. Apr. 201014. Okt. 2014Vcvc Iii LlcSearch and search optimization using a pattern of a location identifier
US886856018. Okt. 200721. Okt. 2014Vcvc Iii LlcSystem and method of a knowledge management and networking environment
US88690236. Aug. 200721. Okt. 2014Ricoh Co., Ltd.Conversion of a collection of data to a structured, printable and navigable format
US8924838 *7. Aug. 200730. Dez. 2014Vcvc Iii Llc.Harvesting data from page
US894985413. März 20123. Febr. 2015International Business Machines CorporationSystem and method for accessing really simple syndication (RSS) enabled content using session initiation protocol (SIP) signaling
US896597924. Apr. 201224. Febr. 2015Vcvc Iii Llc.Methods and systems for semantically managing offers and requests over a network
US902096710. Nov. 200928. Apr. 2015Vcvc Iii LlcSemantically representing a target entity using a semantic object
US903756714. Apr. 201019. Mai 2015Vcvc Iii LlcGenerating user-customized search results and building a semantics-enhanced search engine
US918947912. Sept. 201217. Nov. 2015Vcvc Iii LlcSemantic web portal and platform
US92452527. Mai 200926. Jan. 2016Salesforce.Com, Inc.Method and system for determining on-line influence in social media
US926906816. Okt. 200923. Febr. 2016Visible Technologies LlcSystems and methods for consumer-generated media reputation management
US9292602 *14. Dez. 201022. März 2016Microsoft Technology Licensing, LlcInteractive search results page
US929881415. März 201329. März 2016Maritz Holdings Inc.Systems and methods for classifying electronic documents
US93171808. Juni 201219. Apr. 2016Vocus, Inc.Systems and methods for consumer-generated media reputation management
US9317622 *28. Sept. 201019. Apr. 2016Amazon Technologies, Inc.Methods and systems for fragmenting and recombining content structured language data content to reduce latency of processing and rendering operations
US9448983 *26. Febr. 201420. Sept. 2016Yahoo! Inc.Device, method and computer program product for generating web feeds
US9600593 *30. Sept. 200921. März 2017Brother Kogyo Kabushiki KaishaCommunication system and computer readable medium used therein
US960708914. Okt. 201428. März 2017Vcvc Iii LlcSearch and search optimization using a pattern of a location identifier
US961314918. Apr. 20124. Apr. 2017Vcvc Iii LlcAutomatic mapping of a location identifier pattern of an object to a semantic type using object metadata
US9686341 *20. Aug. 200920. Juni 2017A9.Com, Inc.Review trends
US971054014. März 201618. Juli 2017TSG Technologies, LLCSystems and methods for classifying electronic documents
US20070038646 *4. Aug. 200515. Febr. 2007Microsoft CorporationRanking blog content
US20070061297 *13. Sept. 200515. März 2007Andriy BihunRanking blog documents
US20070078904 *2. Okt. 20065. Apr. 2007Samsung Electronics Co., Ltd.Method and apparatus for publishing content through blog
US20070198491 *22. Dez. 200623. Aug. 2007Hon Hai Precision Industry Co., Ltd.System and method for searching and filtering web pages
US20070239674 *11. Apr. 200611. Okt. 2007Richard GorzelaMethod and System for Providing Weblog Author-Defined, Weblog-Specific Search Scopes in Weblogs
US20070260699 *4. Mai 20068. Nov. 2007Samsung Electronics Co., Ltd.Configurable system for using internet services on CE devices
US20070294252 *19. Juni 200620. Dez. 2007Microsoft CorporationIdentifying a web page as belonging to a blog
US20070294281 *7. Mai 200720. Dez. 2007Miles WardSystems and methods for consumer-generated media reputation management
US20080071819 *14. Sept. 200620. März 2008Jonathan MonsarratAutomatically extracting data and identifying its data type from Web pages
US20080071829 *14. Sept. 200620. März 2008Jonathan MonsarratOnline marketplace for automatically extracted data
US20080104203 *31. Okt. 20061. Mai 2008Microsoft CorporationViewing Digital Information Over a Network
US20080120394 *15. Okt. 200722. Mai 2008Akira YokoyamaNetwork apparatus, data provision location providing method, and recording medium
US20080141113 *11. Dez. 200612. Juni 2008Microsoft CorporationReally simple syndication for data
US20080215607 *27. Febr. 20084. Sept. 2008Umbria, Inc.Tribe or group-based analysis of social media including generating intelligence from a tribe's weblogs or blogs
US20080228695 *21. Juli 200618. Sept. 2008Technorati, Inc.Techniques for analyzing and presenting information in an event-based data aggregation system
US20090030982 *16. Apr. 200829. Jan. 2009Radar Networks, Inc.Methods and systems for semantically managing offers and requests over a network
US20090044106 *6. Aug. 200712. Febr. 2009Kathrin BerknerConversion of a collection of data to a structured, printable and navigable format
US20090070683 *15. Aug. 200812. März 2009Miles WardConsumer-generated media influence and sentiment determination
US20090106697 *14. Okt. 200823. Apr. 2009Miles WardSystems and methods for consumer-generated media reputation management
US20090106768 *23. Okt. 200723. Apr. 2009Sri RamanathanSystem and method for accessing really simple syndication (rss) enabled content using session initiation protocol (sip) signaling
US20090157668 *11. Dez. 200818. Juni 2009Christopher Daniel NewtonMethod and system for measuring an impact of various categories of media owners on a corporate brand
US20090164902 *19. Dez. 200825. Juni 2009Dopetracks, LlcMultimedia player widget and one-click media recording and sharing
US20090192976 *23. Jan. 200930. Juli 2009Radar Networks, Inc.Methods and systems for creating a semantic object
US20090319449 *21. Juni 200824. Dez. 2009Microsoft CorporationProviding context for web articles
US20100005001 *30. Juni 20097. Jan. 2010Aizen JonathanSystems and methods for advertising
US20100005498 *31. Dez. 20087. Jan. 2010Ebay Inc.Systems and methods for publishing and/or sharing media presentations over a network
US20100030753 *31. Juli 20094. Febr. 2010Google Inc.Providing Posts to Discussion Threads in Response to a Search Query
US20100082797 *30. Sept. 20091. Apr. 2010Brother Kogyo Kabushiki KaishaCommunication system and computer readable medium used therein
US20100114814 *17. Nov. 20096. Mai 2010Stragent, LlcOnline marketplace for automatically extracted data
US20100122155 *17. Nov. 200913. Mai 2010Stragent, LlcOnline marketplace for automatically extracted data
US20100256974 *3. Apr. 20097. Okt. 2010Yahoo! Inc.Automated screen scraping via grammar induction
US20110029391 *11. Aug. 20103. Febr. 2011Ryan SteelbergSystem And Method For Metricizing Assets In A Brand Affinity Content Distribution
US20110231385 *16. März 201022. Sept. 2011Microsoft CorporationObject oriented data and metadata based search
US20110302103 *8. Juni 20108. Dez. 2011International Business Machines CorporationPopularity prediction of user-generated content
US20120072835 *20. Sept. 201122. März 2012UberMedia, Inc.Microblog Client
US20120076415 *27. Sept. 201029. März 2012Kahn Michael RComputer aided validation of patent disclosures
US20120150972 *14. Dez. 201014. Juni 2012Microsoft CorporationInteractive search results page
US20120304072 *23. Mai 201129. Nov. 2012Microsoft CorporationSentiment-based content aggregation and presentation
US20140019149 *31. Okt. 201216. Jan. 2014Ricoh Company, Ltd.Scheduling a Patient for a Remote, Virtual Consultation
US20140181642 *26. Febr. 201426. Juni 2014Yahoo! Inc.Device, method and computer program product for generating web feeds
US20150100870 *15. Dez. 20149. Apr. 2015Vcvc Iii LlcHarvesting data from page
CN103198080A *10. Jan. 201210. Juli 2013纽海信息技术(上海)有限公司Homepage display system and method
EP2053522A2 *4. Aug. 200829. Apr. 2009Ricoh Company, Ltd.Conversion of a Collection of Data to a Structured, Printable and Navigable Format
EP2053522A3 *4. Aug. 20088. Juli 2009Ricoh Company, Ltd.Conversion of a Collection of Data to a Structured, Printable and Navigable Format
EP2757450A4 *10. Sept. 201226. Aug. 2015Nec CorpDevice and method for processing write-in information of electronic sticky note
WO2008047137A2 *19. Okt. 200724. Apr. 2008Dovetail Software Corporation LimitedMethod, apparatus and system for preventing web scraping
WO2008047137A3 *19. Okt. 200725. Sept. 2008Dovetail Software Corp LtdMethod, apparatus and system for preventing web scraping
WO2009023865A1 *15. Aug. 200819. Febr. 2009Visible Technologies, Inc.Consumer-generated media influence and sentiment determination
Klassifizierungen
US-Klassifikation1/1, 707/E17.116, 707/999.003
Internationale KlassifikationG06F17/30
UnternehmensklassifikationG06F17/30864, G06F17/3089, G06F17/30424, G06F17/30908, G06F17/30587, G06F17/2705
Europäische KlassifikationG06F17/30S8, G06F17/30W7
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